{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've created a predictive model we can apply this model to a new set of molecules. In many cases, we will build a predictive model based on literature data, then apply that model to a set of molecules that we want to screen.  The molecules we want to screen may come from an internal database or from a commercially available screening collection.  As an example, we will use the predictive model we created to screen a small sample of 100,000 compounds from the ZINC database.  \n",
    "\n",
    "One potential source of difficulty when carrying out a virtual screen is the presence of molecules which have the potential to interfere with biological assays.  Over the last 25 years, many groups have developed sets of computational filters to identify potentially reactive or problematic molecules.  Several of these rule sets, which are encoded as SMARTS strings have been collected by the group that curates the ChEMBL database.  These rule sets have been made avaiable through a Python script called rd_filters.py.  In this example, we will use rd_filters.py to identify potentially problematic molecules in our set of 100,000 molecules from the ZINC database.  \n",
    "\n",
    "**IMPORTANT**  In order to run this notebook you must install the rdfiters script which can be downloaded from https://github.com/PatWalters/rd_filters the script rd_filters must be in your path. \n",
    "\n",
    "The rd_filters script can be called as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage:\r\n",
      "rd_filters filter --in INPUT_FILE --prefix PREFIX [--rules RULES_FILE_NAME] [--alerts ALERT_FILE_NAME][--np NUM_CORES]\r\n",
      "rd_filters template --out TEMPLATE_FILE [--rules RULES_FILE_NAME]\r\n",
      "\r\n",
      "Options:\r\n",
      "--in INPUT_FILE input file name\r\n",
      "--prefix PREFIX prefix for output file names\r\n",
      "--rules RULES_FILE_NAME name of the rules JSON file\r\n",
      "--alerts ALERTS_FILE_NAME name of the structural alerts file\r\n",
      "--np NUM_CORES the number of cpu cores to use (default is all)\r\n",
      "--out TEMPLATE_FILE parameter template file name\r\n"
     ]
    }
   ],
   "source": [
    "!rd_filters -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To call the script on our input file, which is called zinc_100k.smi we can specify the input file and a prefix for output file names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using 8 cores\n",
      "Using alerts from Inpharmatica\n",
      "Wrote SMILES for molecules passing filters to zinc.smi\n",
      "Wrote detailed data to zinc.csv\n",
      "68737 of 100000 passed filters 68.7%\n",
      "Elapsed time 36.55 seconds\n"
     ]
    }
   ],
   "source": [
    "!rd_filters filter --in zinc_100k.smi --prefix zinc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output above indicates the following. \n",
    "* The script runs in parallel across multiple cores, the number of cores can be selected with the \"-np\" flag\n",
    "* The script is using the \"Inpharmatica\" set of alerts. It has 7 other alert sets available.  Please see the rd_filters.py documentaiton for more information\n",
    "* SMILES for the molecules passing the filters was written to a file called zinc.smi.  We will use this as the input when we use the predictive model.\n",
    "* Detailed information on which compounds triggered particular structural alerts was written to a file called zinc.csv. \n",
    "* 68% of the structures passed the filters\n",
    "\n",
    "It is informative to take a look at the reasons molecules were rejected.  This can let us know whether we need to adjust any of the filters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"zinc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>NAME</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>MW</th>\n",
       "      <th>LogP</th>\n",
       "      <th>HBD</th>\n",
       "      <th>HBA</th>\n",
       "      <th>TPSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CN(CCO)C[C@@H](O)Cn1cnc2c1c(=O)n(C)c(=O)n2C</td>\n",
       "      <td>ZINC000000000843</td>\n",
       "      <td>Filter82_pyridinium &gt; 0</td>\n",
       "      <td>311.342</td>\n",
       "      <td>-2.2813</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>105.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>O=c1[nH]c(=O)n([C@@H]2C[C@@H](O)[C@H](CO)O2)cc1Br</td>\n",
       "      <td>ZINC000000001063</td>\n",
       "      <td>Filter9_metal &gt; 0</td>\n",
       "      <td>307.100</td>\n",
       "      <td>-1.0602</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>104.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Cn1c2ncn(CC(=O)N3CCOCC3)c2c(=O)n(C)c1=O</td>\n",
       "      <td>ZINC000000003942</td>\n",
       "      <td>Filter82_pyridinium &gt; 0</td>\n",
       "      <td>307.310</td>\n",
       "      <td>-1.7075</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>91.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CN1C(=O)C[C@H](N2CCN(C(=O)CN3CCCC3)CC2)C1=O</td>\n",
       "      <td>ZINC000000036436</td>\n",
       "      <td>OK</td>\n",
       "      <td>308.382</td>\n",
       "      <td>-1.0163</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>64.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CC(=O)NC[C@H](O)[C@H]1O[C@H]2OC(C)(C)O[C@H]2[C...</td>\n",
       "      <td>ZINC000000041101</td>\n",
       "      <td>OK</td>\n",
       "      <td>302.327</td>\n",
       "      <td>-1.1355</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>106.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                             SMILES  \\\n",
       "0           0        CN(CCO)C[C@@H](O)Cn1cnc2c1c(=O)n(C)c(=O)n2C   \n",
       "1           1  O=c1[nH]c(=O)n([C@@H]2C[C@@H](O)[C@H](CO)O2)cc1Br   \n",
       "2           2            Cn1c2ncn(CC(=O)N3CCOCC3)c2c(=O)n(C)c1=O   \n",
       "3           3        CN1C(=O)C[C@H](N2CCN(C(=O)CN3CCCC3)CC2)C1=O   \n",
       "4           4  CC(=O)NC[C@H](O)[C@H]1O[C@H]2OC(C)(C)O[C@H]2[C...   \n",
       "\n",
       "               NAME                   FILTER       MW    LogP  HBD  HBA  \\\n",
       "0  ZINC000000000843  Filter82_pyridinium > 0  311.342 -2.2813    2    9   \n",
       "1  ZINC000000001063        Filter9_metal > 0  307.100 -1.0602    3    6   \n",
       "2  ZINC000000003942  Filter82_pyridinium > 0  307.310 -1.7075    0    8   \n",
       "3  ZINC000000036436                       OK  308.382 -1.0163    0    5   \n",
       "4  ZINC000000041101                       OK  302.327 -1.1355    3    6   \n",
       "\n",
       "     TPSA  \n",
       "0  105.52  \n",
       "1  104.55  \n",
       "2   91.36  \n",
       "3   64.17  \n",
       "4  106.12  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the Counter class from the Python \"collections\" library to identify which filters were responsible for removing the largest number of molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rule</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OK</td>\n",
       "      <td>69141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Filter41_12_dicarbonyl &gt; 0</td>\n",
       "      <td>19330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Filter82_pyridinium &gt; 0</td>\n",
       "      <td>7720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Filter93_acetyl_urea &gt; 0</td>\n",
       "      <td>1541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Filter78_bicyclic_Imide &gt; 0</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Rule  Count\n",
       "2                            OK  69141\n",
       "7    Filter41_12_dicarbonyl > 0  19330\n",
       "0       Filter82_pyridinium > 0   7720\n",
       "11     Filter93_acetyl_urea > 0   1541\n",
       "12  Filter78_bicyclic_Imide > 0    825"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "count_list = list(Counter(df.FILTER).items())\n",
    "count_df = pd.DataFrame(count_list,columns=[\"Rule\",\"Count\"])\n",
    "count_df.sort_values(\"Count\",inplace=True,ascending=False)\n",
    "count_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest number of molecules (19,330) were rejected because they contained a 1,2 dicarbonyl group.  Molecules of this type have a tendency to act a Michael Acceptors and may reactive with nucleophilic protein residues such as serine and cysteine. Let's take a look at a few of these molecules.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_list = df[df.FILTER == \"Filter41_12_dicarbonyl > 0\"].SMILES[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x400 at 0x7F31F4698F60>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "\n",
    "mol_list = [Chem.MolFromSmiles(x) for x in smiles_list]\n",
    "dicarbonyl = Chem.MolFromSmarts('*C(=O)C(=O)*')\n",
    "match_list = [mol.GetSubstructMatch(dicarbonyl) for mol in mol_list]\n",
    "Draw.MolsToGridImage(mol_list,highlightAtomLists=match_list,molsPerRow=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the molecules do indeed have dicarbonyl groups.  If we wanted to we could similarly evaluate other filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
